In the context of distributed data management systems, event models are sometimes used to provide information about data changes. In a typical example, a client can register a listener to listen for a particular event on a piece of data. When the data changes, an event is generated and the client is informed of the event by way of a listener.
One limitation with this approach is that it is asynchronous in the sense that the event has already occurred, meaning that the client has no effective means to affect the data change because the client is informed after the event has already occurred. Thus, to make a change to the event, the client may be forced to perform a whole new transaction to make that change. Another limitation is often the size and the scalability of the solution because the client is registered to listen to all events in what is a frequently very large data set.
Additionally, various extensible hooks with different semantics and idiosyncratic forms of configuration are often presented to users as separate features. There can be little documentation about how these hooks relate to one another and it is not always clear which one is the most appropriate to use. In addition, the programming model varies for each hook, many of which have unique limitations.
A new solution is thus desirable, one which would address or resolve the above limitations as well as provide a number of other advantages.